With the accelerating adoption of networked service provider environments (e.g., “cloud” computing) around the world, organizations are increasingly seeking ways to manage large amounts of data and large-scale computing resources. Services have been developed for real-time processing of streaming data at massive scales. For example, a streaming analytics service may collect and process hundreds of terabytes of data per hour from hundreds of thousands of sources. Such a service enables developers to write applications that process information in real-time from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, operational logs, metering data and so forth.
Additionally, a streaming analytics service may enable developers to build real-time dashboards, capture exceptions and generate alerts, drive recommendations, and make other real-time business or operational decisions. Applications can be built which respond to changes in the streaming data in seconds, at any scale. Stream data may be stored across multiple availability zones in a region for a set time window. During that window, data is available to be read, re-read, backfilled, analyzed or moved to long-term storage. Developers can focus on creating business applications while offloading the burden associated with load-balancing streaming data, coordinating distributed services, and fault-tolerant data processing. Despite the benefits of a streaming analytics service, improvements may yet be made to improve the value and availability of streaming analytics data.